Dataset Information

Probabilistic walking models using built environment and sociodemographic predictors.

ABSTRACT:

Background

Individual sociodemographic and home neighborhood built environment (BE) factors influence the probability of engaging in health-enhancing levels of walking or moderate-to-vigorous physical activity (MVPA). Methods are needed to parsimoniously model the associations.

Methods

Participants included 2392 adults drawn from a community-based twin registry living in the Seattle region. Objective BE measures from four domains (regional context, neighborhood composition, destinations, transportation) were taken for neighborhood sizes of 833 and 1666 road network meters from home. Hosmer and Lemeshow's methods served to fit logistic regression models of walking and MVPA outcomes using sociodemographic and BE predictors. Backward elimination identified variables included in final models, and comparison of receiver operating characteristic (ROC) curves determined model fit improvements.

Results

Built environment variables associated with physical activity were reduced from 86 to 5 or fewer. Sociodemographic and BE variables from all four BE domains were associated with activity outcomes but differed by activity type and neighborhood size. For the study population, ROC comparisons indicated that adding BE variables to a base model of sociodemographic factors did not improve the ability to predict walking or MVPA.

Conclusions

Using sociodemographic and built environment factors, the proposed approach can guide the estimation of activity prediction models for different activity types, neighborhood sizes, and discrete BE characteristics. Variables associated with walking and MVPA are population and neighborhood BE-specific.

SUBMITTER: Moudon AV

PROVIDER: S-EPMC6547573 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Probabilistic walking models using built environment and sociodemographic predictors.

Moudon Anne Vernez AV Huang Ruizhu R Stewart Orion T OT Cohen-Cline Hannah H Noonan Carolyn C Hurvitz Philip M PM Duncan Glen E GE

Population health metrics 20190603 1

<h4>Background</h4>Individual sociodemographic and home neighborhood built environment (BE) factors influence the probability of engaging in health-enhancing levels of walking or moderate-to-vigorous physical activity (MVPA). Methods are needed to parsimoniously model the associations.<h4>Methods</h4>Participants included 2392 adults drawn from a community-based twin registry living in the Seattle region. Objective BE measures from four domains (regional context, neighborhood composition, destin ...[more]

PMID: 31159824

Similar Datasets

Project description:BackgroundPerceptions of the built environment, such as nature quality, beauty, relaxation, and safety, may be key factors linking the built environment to human health. However, few studies have examined these types of perceptions due to the difficulty in quantifying them objectively in large populations.ObjectiveTo measure and predict perceptions of the built environment from street-view images using crowd-sourced methods and deep learning models for application in epidemiologic studies.MethodsWe used the Amazon Mechanical-Turk crowdsourcing platform where participants compared two street-view images and quantified perceptions of nature quality, beauty, relaxation, and safety. We optimized street-view image sampling methods to improve the quality and resulting perception data specific to participants enrolled in the Washington State Twin Registry (WSTR) health study. We used a transfer learning approach to train deep learning models by leveraging existing image perception data from the PlacePulse 2.0 dataset, which includes 1.1 million image comparisons, and refining based on new WSTR perception data. Resulting models were applied to WSTR addresses to estimate exposures and evaluate associations with traditional built environment measures.ResultsWe collected over 36,000 image comparisons and calculated perception measures for each image. Our final deep learning models explained 77.6% of nature quality, 68.1% of beauty, 72.0% of relaxation, and 64.7% of safety in pairwise image comparisons. Applying transfer learning with the new perception labels specific to the WSTR yielded an average improvement of 3.8% for model performance. Perception measures were weakly to moderately correlated with traditional built environment exposures for WSTR participant addresses; for example, nature quality and NDVI (r = 0.55), neighborhood area deprivation (r = -0.16), and walkability (r = -0.20), respectively.SignificanceWe were able to measure and model perceptions of the built environment optimized for a specific health study. Future applications will examine associations between these exposure measures and mental health in the WSTR.Impact statementBuilt environments influence health through complex pathways. Perceptions of nature quality, beauty, relaxation and safety may be particularly import for understanding these linkages, but few studies to-date have examined these perceptions objectively for large populations. For quantitative research, an exposure measure must be reproducible, accurate, and precise--here we work to develop such measures for perceptions of the urban environment. We created crowd-sourced and image-based deep learning methods that were able to measure and model these perceptions. Future applications will apply these models to examine associations with mental health in the Washington State Twin Registry.

Dataset Information

Probabilistic walking models using built environment and sociodemographic predictors.

Background

Methods

Results

Conclusions

Publications

Probabilistic walking models using built environment and sociodemographic predictors.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets